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# Autogenerated By   : src/main/python/generator/generator.py
# Autogenerated From : scripts/builtin/cspline.dml

from typing import Dict, Iterable

from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar
from systemds.script_building.dag import OutputType
from systemds.utils.consts import VALID_INPUT_TYPES


def cspline(X: Matrix,
            Y: Matrix,
            inp_x: float,
            **kwargs: Dict[str, VALID_INPUT_TYPES]):
    """
     Solves Cubic Spline Interpolation
    
     Algorithms: implement https://en.wikipedia.org/wiki/Spline_interpolation#Algorithm_to_find_the_interpolating_cubic_spline
     It use natural spline with q1''(x0) == qn''(xn) == 0.0
    
    
    
    :param X: 1-column matrix of x values knots. It is assumed that x values are
        monotonically increasing and there is no duplicates points in X
    :param Y: 1-column matrix of corresponding y values knots
    :param inp_x: the given input x, for which the cspline will find predicted y
    :param mode: Specifies the method for cspline (DS - Direct Solve, CG - Conjugate Gradient)
    :param tol: Tolerance (epsilon); conjugate graduent procedure terminates early if
        L2 norm of the beta-residual is less than tolerance * its initial norm
    :param maxi: Maximum number of conjugate gradient iterations, 0 = no maximum
    :return: Predicted value
    :return: Matrix of k parameters
    """

    params_dict = {'X': X, 'Y': Y, 'inp_x': inp_x}
    params_dict.update(kwargs)
    
    vX_0 = Matrix(X.sds_context, '')
    vX_1 = Matrix(X.sds_context, '')
    output_nodes = [vX_0, vX_1, ]

    op = MultiReturn(X.sds_context, 'cspline', output_nodes, named_input_nodes=params_dict)

    vX_0._unnamed_input_nodes = [op]
    vX_1._unnamed_input_nodes = [op]

    return op
